收藏切换
Study on Rock Blastability Classification of Deep Phosphate Ore Rock Mass based on Neural Network
收藏切换
PDF
Xiu-wei CHAI1, Cheng-zhen LI1, Yi-ming SHENG1, Yu-ping XU2, Liang XU3, Sheng-li JIN3
Blasting | 2025, 42(1) : 71 - 80
Less
收藏切换
Blasting | 2025, 42(1): 71-80
BLASTING IN ORE AND ROCK
Study on Rock Blastability Classification of Deep Phosphate Ore Rock Mass based on Neural Network
Full
Xiu-wei CHAI1, Cheng-zhen LI1, Yi-ming SHENG1, Yu-ping XU2, Liang XU3, Sheng-li JIN3
Affiliations
  • 1.School of Resources and Safety Engineering, Wuhan Institute of Technology, Wuhan 430073, China
  • 2.School of Environmental and Biological Engineering, Wuhan Technology And Business University, Wuhan 430065, China
  • 3.Hubei Xingfa Chemical Group Co., Ltd., Yichang 443700, China
Published: 2025-05-20 doi: 10.3963/j.issn.1001-487X.2025.01.009
Outline
收藏切换

Drilling and blasting is still the most efficient way to explore deep phosphate mine excavation and mining. There is a severe constraint on the efficiency of phosphate mine digging as its level remained at 70 to 80 meters every month for many years. Therefore, the ore rock blastability classification is critical for the deep phosphate mine working face. The longitudinal wave velocity tests of the rock body in an underground phosphate mine in Yichang, Hubei Province, and measurements of physical and mechanical properties such as rock density, uniaxial compressive strength and tensile strength were carried out. The rock density, uniaxial compressive strength, tensile strength, and rock integrity coefficient were obtained for four types of rocks, namely, dolomitic striped phosphorite, dense striped phosphorite, argillaceous striped phosphorite, and carbon-bearing argillaceous dolomite. To complete the deep phosphorite workings of the mine rock blastability classification, a BP neural network model was established by stochastic functions to generate a large number of learning and testing samples using the Matlab neural network toolbox as taking the pre-measured rock density, uniaxial compressive strength, tensile strength and rock integrity coefficients as inputs and the rock blastability classification as outputs. The grading results show that dolomite-banded phosphorite and mud-banded phosphorite are moderately blastable, and dense-banded phosphorite and carbonaceous mud dolomite are difficult to blast. According to the classification results, the blasting parameters of the stope can be optimized to enhance the blasting effect, reduce the single consumption and the bulk rate of explosives, and improve the safety and economic benefits of deep phosphate mining.

deep phosphate ore  /  rock blastability classification  /  random function  /  neural network model
Xiu-wei CHAI, Cheng-zhen LI, Yi-ming SHENG, Yu-ping XU, Liang XU, Sheng-li JIN. Study on Rock Blastability Classification of Deep Phosphate Ore Rock Mass based on Neural Network[J]. Blasting, 2025 , 42 (1) : 71 -80 . DOI: 10.3963/j.issn.1001-487X.2025.01.009
  • 2021 Hubei Province Safety Production Special Fund Science and Technology Project(SJZX20211004)
  • Special Funding from the Scientific Research Team Support Program of Wuhan Technology and Business University(WPT2023036)
Year 2025 volume 42 Issue 1
PDF
90
43
Cite this Article
BibTeX
Article Info
doi: 10.3963/j.issn.1001-487X.2025.01.009
  • Receive Date:2024-04-11
  • Online Date:2026-03-18
  • Published:2025-05-20
Article Data
Affiliations
History
  • Received:2024-04-11
Funding
2021 Hubei Province Safety Production Special Fund Science and Technology Project(SJZX20211004)
Special Funding from the Scientific Research Team Support Program of Wuhan Technology and Business University(WPT2023036)
Affiliations
    1.School of Resources and Safety Engineering, Wuhan Institute of Technology, Wuhan 430073, China
    2.School of Environmental and Biological Engineering, Wuhan Technology And Business University, Wuhan 430065, China
    3.Hubei Xingfa Chemical Group Co., Ltd., Yichang 443700, China

Corresponding:

XU Yu-ping(1981-), Female, Shiyan, HubeiProvince, Master, Associate Professor, mine environmental engineering, (E-mail) .
References
Share
https://castjournals.cast.org.cn/joweb/bp/EN/10.3963/j.issn.1001-487X.2025.01.009
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT